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  • Protocol Extension
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Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions

Nature Protocols volume 17, pages 2415–2430 (2022)Cite this article

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Abstract

Lipidomics studies suffer from analytical and annotation challenges because of the great structural similarity of many of the lipid species. To improve lipid characterization and annotation capabilities beyond those afforded by traditional mass spectrometry (MS)-based methods, multidimensional separation methods such as those integrating liquid chromatography, ion mobility spectrometry, collision-induced dissociation and MS (LC-IMS-CID-MS) may be used. Although LC-IMS-CID-MS and other multidimensional methods offer valuable hydrophobicity, structural and mass information, the files are also complex and difficult to assess. Thus, the development of software tools to rapidly process and facilitate confident lipid annotations is essential. In this Protocol Extension, we use the freely available, vendor-neutral and open-source software Skyline to process and annotate multidimensional lipidomic data. Although Skyline (https://skyline.ms/skyline.url) was established for targeted processing of LC-MS-based proteomics data, it has since been extended such that it can be used to analyze small-molecule data as well as data containing the IMS dimension. This protocol uses Skyline’s recently expanded capabilities, including small-molecule spectral libraries, indexed retention time and ion mobility filtering, and provides a step-by-step description for importing data, predicting retention times, validating lipid annotations, exporting results and editing our manually validated 500+ lipid library. Although the time required to complete the steps outlined here varies on the basis of multiple factors such as dataset size and familiarity with Skyline, this protocol takes ~5.5 h to complete when annotations are rigorously verified for maximum confidence.

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Fig. 1: Lipidomic LC-IMS-CID-MS protocol overview.
Fig. 2: Summary of library lipids.
Fig. 3: Skyline spectral library explorer view.
Fig. 4: Precursor chromatogram for positive-mode iRT calibrants.
Fig. 5: Annotation validation.
Fig. 6: Annotation validation example.
Fig. 7: Lipid MS/MS spectra example.
Fig. 8: Skyline replicate comparisons.
Fig. 9: iRT performance assessment.

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Data availability

All associated library and data files are publicly available at Panorama Public (https://panoramaweb.org/baker-lipid-ims.url) and Zenodo (https://zenodo.org/record/6374209#.YpzPMxPMJJU)47.

Code availability

Skyline source code is freely available at https://skyline.ms/source.url.

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Acknowledgements

Portions of this research were supported by grants from the NIH National Institute of Environmental Health Sciences (P30 ES025128, P42 ES027704 and P42 ES031009), NIH National Institute of General Medicine Sciences (R24 GM141156, P41 GM103533 and T32 GM133366), a cooperative agreement with the United States Environmental Protection Agency (STAR RD 84003201) and startup funds from North Carolina State University. In addition, most of the LC-IMS-CID-MS measurements were made in the Molecular Education, Technology, and Research Innovation Center (METRIC) at North Carolina State University.

Author information

Authors and Affiliations

  1. Department of Chemistry, North Carolina State University, Raleigh, NC, USA

    Kaylie I. Kirkwood & Erin S. Baker

  2. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Brian S. Pratt, Nicholas Shulman, Kaipo Tamura, Michael J. MacCoss & Brendan X. MacLean

  3. Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA

    Erin S. Baker

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Contributions

K.I.K., B.S.P., N.S., K.T., M.J.M., B.X.M. and E.S.B. developed and optimized the protocol and Skyline tools. K.I.K. drafted the text of the manuscript.

Corresponding author

Correspondence to Erin S. Baker.

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The authors declare no competing interests.

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Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Kirkwood, K. I. et al. J. Proteome Res. 21, 232–242 (2022): https://doi.org/10.1021/acs.jproteome.1c00820

Odenkirk, M. T. et al. ACS Meas. Sci. Au 2, 67–75 (2022): https://doi.org/10.1021/acsmeasuresciau.1c00035

Key data used in this protocol

NCSU Baker Lab—Lipid Libraries: https://panoramaweb.org/baker-lipid-ims.url

Kirkwood, K. I. et al. Zenodo https://doi.org/10.5281/zenodo.6374209

This protocol is an extension to: Nat. Protoc. 10, 887–903 (2015): https://doi.org/10.1038/nprot.2015.055

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Tables 3–5 and Supplementary Figs. 1–17.

Supplementary Table 1

Transition lists

Supplementary Table 2

iRT prediction results

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Kirkwood, K.I., Pratt, B.S., Shulman, N. et al. Utilizing Skyline to analyze lipidomics data containing liquid chromatography, ion mobility spectrometry and mass spectrometry dimensions. Nat Protoc 17, 2415–2430 (2022). https://doi.org/10.1038/s41596-022-00714-6

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